PROJECT SUMMARY This proposal aims to elucidate the computational and neural basis of neuroprosthetic skill learning by leveraging recent advances in the science and engineering of closed-loop brain-machine interfacing. The outcome of the proposed work has the potential to guide the development of the next generation of neurophysiologically-informed, cortically-controlled neuroprosthetic systems for patients with neurological disorders. State-of-the-art brain-machine interfaces (BMIs) leverage machine learning to rapidly calibrate to the neural activity of individuals, but performance also benefits from subjects learning to reliably produce desired neural activity patterns. The basic science and engineering principles of designing such a ?2-learner BMI? in which the brain and machine synergistically learn are not well understood. Hence, this proposal aims to investigate how the brain learns when the machine undergoes different degrees of learning, how different degrees of brain learning affect long-term BMI performance, robustness, and generalization, and how these principles can guide the design of a 2-learner BMI system which facilitates brain learning. The proposal is structured in three aims: 1) To study the impact of decoder adaptation on the development of neural encoding models underlying neuroprosthetic skill; 2) To Study how decoder adaptation and resultant neural encoding model influences BMI performance with perturbations (robustness) and BMI performance on unpracticed tasks (generalization); and 3) Design and validation of the next-generation Flexible 2-Learner Decoder architecture. The analyses and experiments proposed in these aims will leverage the fundamental knowledge gained about how the brain learns and acquires neuroprosthetic skills into the neurophysiologically-informed design of robust and high-performance closed-loop motor neuroprosthetics that generalize to new tasks.